Measure test-matrix saturation

Project.toml

@@ -1,7 +1,7 @@
 name = "ScaleInvariantAnalysis"
 uuid = "727e6139-ff52-4636-a344-ed1d23e73ffc"
-authors = ["Tim Holy <tim.holy@gmail.com> and contributors"]
 version = "1.0.0-DEV"
+authors = ["Tim Holy <tim.holy@gmail.com> and contributors"]
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -23,6 +23,7 @@ LinearAlgebra = "1.10.0"
 NautyGraphs = "0.7"
 ParametricOptInterface = "0.15"
 PrecompileTools = "1"
+ProgressMeter = "1"
 Random = "1"
 SparseArrays = "1.10.0"
 julia = "1.10.10"
@@ -33,9 +34,10 @@ HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 NautyGraphs = "7509a0a4-015a-4167-b44b-0799a1a2605e"
 ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Graphs", "HiGHS", "JuMP", "NautyGraphs", "ParametricOptInterface", "Random", "Statistics", "Test"]
+test = ["Graphs", "HiGHS", "JuMP", "NautyGraphs", "ParametricOptInterface", "ProgressMeter", "Random", "Statistics", "Test"]

test/buildmatrices.jl

@@ -2,6 +2,7 @@ using LinearAlgebra
 using Graphs: Graphs, connected_components
 using NautyGraphs: NautyGraphs, NautyGraph, NautyDiGraph, canonize!
 using Random
+using ProgressMeter
 
 
 # The following partially duplicate "testutils.jl", but with an emphasis on performance in generating
@@ -55,7 +56,7 @@ function logA_reset!(ps, logA)
 end
 
 
-function accumulate_examples!(fdA, NG, comps, A, model, ps, vars...)
+function accumulate_examples!(fdA, NG, comps, lenhist, i, A, model, ps, vars...)
     logA = log.(abs.(A))
     logA_reset!(ps, logA)
     dA = fdA(model, A, logA, vars...)
@@ -69,6 +70,7 @@ function accumulate_examples!(fdA, NG, comps, A, model, ps, vars...)
     end
     if !haskey(comps, edges)
         comps[edges] = A[p, p]
+        push!(lenhist, i)
     end
 end
 
@@ -77,6 +79,7 @@ function dA_symmetric(model, A, logA, αvariable)
             JuMP.optimize!(model)
             JuMP.value.(αvariable)
         catch err
+            # xref https://github.com/jump-dev/HiGHS.jl/issues/322
             @warn "Optimization failed for matrix $A: $err"
             return
         end
@@ -98,19 +101,21 @@ Random.seed!(1234)
 
 valrange = 1:20
 comps_sym = Dict{Vector{Tuple{Int,Int}}, Matrix{Int}}()
+lenhist_sym = Int[]
 Adummy = Symmetric([rand(valrange) for _ in 1:5, _ in 1:5])
 model, αvariable, ps = logsymcover_ref_setup(log.(abs.(Adummy)))
-for i = 1:10^5
+@showprogress "Building random symmetric matrices..." for i = 1:10^5
     A = Symmetric([rand(valrange) for _ in 1:5, _ in 1:5])
-    accumulate_examples!(dA_symmetric, NautyGraph, comps_sym, A, model, ps, αvariable)
+    accumulate_examples!(dA_symmetric, NautyGraph, comps_sym, lenhist_sym, i, A, model, ps, αvariable)
 end
 
 comps = Dict{Vector{Tuple{Int,Int}}, Matrix{Int}}()
+lenhist = Int[]
 Adummy = [rand(valrange) for _ in 1:5, _ in 1:5]
 model, αvariable, βvariable, ps = logcover_ref_setup(log.(abs.(Adummy)))
-for i = 1:10^5
+@showprogress "Building random general matrices..." for i = 1:10^5
     A = [rand(valrange) for _ in 1:5, _ in 1:5]
-    accumulate_examples!(dA_general, NautyDiGraph, comps, A, model, ps, αvariable, βvariable)
+    accumulate_examples!(dA_general, NautyDiGraph, comps, lenhist, i, A, model, ps, αvariable, βvariable)
 end